Drug-induced Perturbations Research Articles

The mode of action of sorafenib in MDA-MB-231 breast carcinoma cells involves components of apoptotic, necroptotic and autophagy-dependent cell death pathways

We report the identification of an interesting mode of action by sorafenib (SF) (Nexavar) in triple-negative breast adenocarcinoma MDA-MB-231 cells. The dying cells presented features of apoptosis, such as externalization of phosphatidylserine and cleaved caspase-3, and autophagy-mediated cell death, such as formation of autophagosomes and autolysosomes, the overexpression of LC3-II, and the presence of LAMP1-positive vacuoles, while displaying insufficient autophagic flux. Components of endoplasmic reticulum stress (ER stress; PERK and CHOP) and of necroptosis (p-MLKL) were also elevated considerably. Investigating potential target proteins that could modulate this form of cell death, we next investigated the role of tubulin disruption, which is known to induce necroptosis, apoptosis, and autophagy-dependent cell death. Interactions of SF with purified tubulin were investigated in detail using a combination of cellular and biophysical assays, transmission electron microscopy, and computer simulations. A marked reduction in the intrinsic tryptophan fluorescence of tubulin, a concentration-dependent elevation of anilinonaphthalene sulfonate–tubulin complex fluorescence, electron micrographs of deformed in vitro–assembled microtubules, and disrupted and hyper-stabilized cellular microtubules evinced the ability of SF to target tubulin and disrupt cellular microtubules. Molecular docking and molecular dynamic simulations positioned the drug between the α and β subunits of tubulin with considerable stability (ΔGbind, −31.43 kcal/mol), suggesting that drug-induced perturbation of tubulin could contribute to this mode of cell death.

Secretion of acetylated amino acids by drug-induced cancer cells: perspectives on metabolic-epigenetic alterations.

The emerging understanding of the super-complex and heterogeneous nature of tumor is well supported by metabolic reprogramming, leading survival advantages. Metabolic reprogramming contributes to tumor responsiveness and resistance to various antitumor drugs. Among the numerous adaptations made by cancer cells in response to drug-induced perturbations, key metabolic alterations involving amino acids and acetylated derivatives of amino acids have received special attention. Considering theseimplications discussed, targeting cancer-associated metabolic pathways, particularly those involving acetylated amino acids, emerges as an important avenue in the pursuit of combinatorial anticancer strategies. As a result, the introduction of mimetic acetylated amino acids represents a promising new class of inhibitors that could be used alongside traditional chemotherapy agents.

A Method to Monitor the NAD+ Metabolome-From Mechanistic to Clinical Applications.

Nicotinamide adenine dinucleotide (NAD+) and its reduced form (NADH) are coenzymes employed in hundreds of metabolic reactions. NAD+ also serves as a substrate for enzymes such as sirtuins, poly(ADP-ribose) polymerases (PARPs) and ADP-ribosyl cyclases. Given the pivotal role of NAD(H) in health and disease, studying NAD+ metabolism has become essential to monitor genetic- and/or drug-induced perturbations related to metabolic status and diseases (such as ageing, cancer or obesity), and its possible therapies. Here, we present a strategy based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), for the analysis of the NAD+ metabolome in biological samples. In this method, hydrophilic interaction chromatography (HILIC) was used to separate a total of 18 metabolites belonging to pathways leading to NAD+ biosynthesis, including precursors, intermediates and catabolites. As redox cofactors are known for their instability, a sample preparation procedure was developed to handle a variety of biological matrices: cell models, rodent tissues and biofluids, as well as human biofluids (urine, plasma, serum, whole blood). For clinical applications, quantitative LC-MS/MS for a subset of metabolites was demonstrated for the analysis of the human whole blood of nine volunteers. Using this developed workflow, our methodology allows studying NAD+ biology from mechanistic to clinical applications.

Computational Methods for Structure-to-Function Analysis of Diet-Derived Catechins-Mediated Targeting of In Vitro Vasculogenic Mimicry.

Background:Vasculogenic mimicry (VM) is an adaptive biological phenomenon wherein cancer cells spontaneously self-organize into 3-dimensional (3D) branching network structures. This emergent behavior is considered central in promoting an invasive, metastatic, and therapy resistance molecular signature to cancer cells. The quantitative analysis of such complex phenotypic systems could require the use of computational approaches including machine learning algorithms originating from complexity science.Procedures:In vitro 3D VM was performed with SKOV3 and ES2 ovarian cancer cells cultured on Matrigel. Diet-derived catechins disruption of VM was monitored at 24 hours with pictures taken with an inverted microscope. Three computational algorithms for complex feature extraction relevant for 3D VM, including 2D wavelet analysis, fractal dimension, and percolation clustering scores were assessed coupled with machine learning classifiers.Results:These algorithms demonstrated the structure-to-function galloyl moiety impact on VM for each of the gallated catechin tested, and shown applicable in quantifying the drug-mediated structural changes in VM processes.Conclusions:Our study provides evidence of how appropriate 3D VM compression and feature extractors coupled with classification/regression methods could be efficient to study in vitro drug-induced perturbation of complex processes. Such approaches could be exploited in the development and characterization of drugs targeting VM.

Bis(monoacylglycero)phosphate, a new lipid signature of endosome-derived extracellular vesicles

Bis(monoacylglycero)phosphate (BMP), also known as lysobisphosphatidic acid (LBPA), is a phospholipid specifically enriched in the late endosome-lysosome compartment playing a crucial role for the fate of endocytosed components. Due to its presence in extracellular fluids during diseases associated with endolysosomal dysfunction, it is considered as a possible biomarker of disorders such as genetic lysosomal storage diseases and cationic amphiphilic drug-induced phospholipidosis. However, there is no true validation of this biomarker in human studies, nor a clear identification of the carrier of this endolysosome-specific lipid in biofluids. The present study demonstrates that in absence of any sign of renal failure, BMP, especially all docosahexaenoyl containing species, are significantly increased in the urine of patients treated with the antiarrhythmic drug amiodarone. Such urinary BMP increase could reflect a generalized drug-induced perturbation of the endolysosome compartment as observed in vitro with amiodarone-treated human macrophages. Noteworthy, BMP was associated with extracellular vesicles (EVs) isolated from human urines and extracellular medium of human embryonic kidney HEK293 cells and co-localizing with classical EV protein markers CD63 and ALIX. In the context of drug-induced endolysosomal dysfunction, increased BMP-rich EV release could be useful to remove excess of undigested material. This first human pilot study not only reveals BMP as a urinary biomarker of amiodarone-induced endolysosomal dysfunction, but also highlights its utility to prove the endosomal origin of EVs, also named as exosomes. This peculiar lipid already known as a canonical late endosome-lysosome marker, may be thus considered as a new lipid marker of urinary exosomes.

Rapid exposure of macrophages to drugs resolves four classes of effects on the leading edge sensory pseudopod: Non-perturbing, adaptive, disruptive, and activating.

Leukocyte migration is controlled by a membrane-based chemosensory pathway on the leading edge pseudopod that guides cell movement up attractant gradients during the innate immune and inflammatory responses. This study employed single cell and population imaging to investigate drug-induced perturbations of leading edge pseudopod morphology in cultured, polarized RAW macrophages. The drugs tested included representative therapeutics (acetylsalicylic acid, diclofenac, ibuprofen, acetaminophen) as well as control drugs (PDGF, Gö6976, wortmannin). Notably, slow addition of any of the four therapeutics to cultured macrophages, mimicking the slowly increasing plasma concentration reported for standard oral dosage in patients, yielded no detectable change in pseudopod morphology. This finding is consistent with the well established clinical safety of these drugs. However, rapid drug addition to cultured macrophages revealed four distinct classes of effects on the leading edge pseudopod: (i) non-perturbing drug exposures yielded no detectable change in pseudopod morphology (acetylsalicylic acid, diclofenac); (ii) adaptive exposures yielded temporary collapse of the extended pseudopod and its signature PI(3,4,5)P3 lipid signal followed by slow recovery of extended pseudopod morphology (ibuprofen, acetaminophen); (iii) disruptive exposures yielded long-term pseudopod collapse (Gö6976, wortmannin); and (iv) activating exposures yielded pseudopod expansion (PDGF). The novel observation of adaptive exposures leads us to hypothesize that rapid addition of an adaptive drug overwhelms an intrinsic or extrinsic adaptation system yielding temporary collapse followed by adaptive recovery, while slow addition enables gradual adaptation to counteract the drug perturbation in real time. Overall, the results illustrate an approach that may help identify therapeutic drugs that temporarily inhibit the leading edge pseudopod during extreme inflammation events, and toxic drugs that yield long term inhibition of the pseudopod with negative consequences for innate immunity. Future studies are needed to elucidate the mechanisms of drug-induced pseudopod collapse, as well as the mechanisms of adaptation and recovery following some inhibitory drug exposures.

Bayesian Integrative Modeling of Genome-Scale Metabolic and Regulatory Networks

The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance to guide the search for various applications such as discovering novel potential drug targets to develop efficient therapeutic strategies for various diseases. In this paper, we propose an integrative predictive model based on techniques combining semantic web, probabilistic modeling, and constraint-based modeling methods. We applied our approach to human cancer metabolism to predict in silico the growth response of specific cancer cells under approved drug effects. Our method has proven successful in predicting the biomass rates of human liver cancer cells under drug-induced transcriptional perturbations.

Immunopathologic Effects of Prednisolone and Cyclosporine A on Feline Immunodeficiency Virus Replication and Persistence.

Feline immunodeficiency virus (FIV) induces opportunistic disease in chronically infected cats, and both prednisolone and cyclosporine A (CsA) are clinically used to treat complications such as lymphoma and stomatitis. However, the impact of these compounds on FIV infection are still unknown and understanding immunomodulatory effects on FIV replication and persistence is critical to guide safe and effective therapies. To determine the immunologic and virologic effects of prednisolone and CsA during FIV infection, FIV-positive cats were administered immunosuppressive doses of prednisolone (2 mg/kg) or CsA (5 mg/kg). Both prednisolone and CsA induced acute and transient increases in FIV DNA and RNA loads as detected by quantitative PCR. Changes in the proportion of lymphocyte immunophenotypes were also observed between FIV-infected and naïve cats treated with CsA and prednisolone, and both treatments caused acute increases in CD4+ lymphocytes that correlated with increased FIV RNA. CsA and prednisolone also produced alterations in cytokine expression that favored a shift toward a Th2 response. Pre-treatment with CsA slightly enhanced the efficacy of antiretroviral therapy but did not enhance clearance of FIV. Results highlight the potential for drug-induced perturbation of FIV infection and underscore the need for more information regarding immunopathologic consequences of therapeutic agents on concurrent viral infections.

Dr.VAE: improving drug response prediction via modeling of drug perturbation effects

MotivationIndividualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover biomarkers or to develop machine learning methods for accurate drug response prediction in cancers. Incorporating prior knowledge of biological systems into these methods is a promising avenue to improve prediction performance. High-throughput cell line assays of drug-induced transcriptomic perturbation effects are a prior knowledge that has not been fully incorporated into a drug response prediction model yet.ResultsWe introduce a unified probabilistic approach, Drug Response Variational Autoencoder (Dr.VAE), that simultaneously models both drug response in terms of viability and transcriptomic perturbations. Dr.VAE is a deep generative model based on variational autoencoders. Our experimental results showed Dr.VAE to do as well or outperform standard classification methods for 23 out of 26 tested Food and Drug Administration-approved drugs. In a series of ablation experiments we showed that the observed improvement of Dr.VAE can be credited to the incorporation of drug-induced perturbation effects with joint modeling of treatment sensitivity.Availability and implementationProcessed data and software implementation using PyTorch (Paszke et al., 2017) are available at: https://github.com/rampasek/DrVAE.Supplementary information Supplementary data are available at Bioinformatics online.

Ketamine and Ceftriaxone-Induced Alterations in Glutamate Levels Do Not Impact the Specific Binding of Metabotropic Glutamate Receptor Subtype 5 Radioligand [18F]PSS232 in the Rat Brain.

Several studies showed that [11C]ABP688 binding is altered following drug-induced perturbation of glutamate levels in brains of humans, non-human primates and rats. We evaluated whether the fluorinated derivative [18F]PSS232 can be used to assess metabotropic glutamate receptor 5 (mGluR5) availability in rats after pharmacological challenge with ketamine, known to increase glutamate, or ceftriaxone, known to decrease glutamate. In vitro autoradiography was performed on rat brain slices with [18F]PSS232 to prove direct competition of the drugs for mGluR5. One group of rats were challenged with a bolus injection of either vehicle, racemic ketamine, S-ketamine or ceftriaxone followed by positron emission tomography PET imaging with [18F]PSS232. The other group received an infusion of the drugs during the PET scan. Distribution volume ratios (DVRs) were calculated using a reference tissue model. In vitro autoradiography showed no direct competition of the drugs with [18F]PSS232 for the allosteric binding site of mGluR5. DVRs of [18F]PSS232 binding in vivo did not change in any brain region neither after bolus injection nor after infusion. We conclude that [18F]PSS232 has utility for measuring mGluR5 density or occupancy of the allosteric site in vivo, but it cannot be used to measure in vivo fluctuations of glutamate levels in the rat brain.

Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation

Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis, which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.

Multinuclear NMR and MRI Reveal an Early Metabolic Response to mTOR Inhibition in Sarcoma.

Biomarkers predicting rapalog responses in sarcomas where PI3K and mTOR are often hyperactivated could improve the suitable recruitment of responsive patients to clinical trials. PI3K/mTOR pathway activation drives energy production by regulating anaerobic glycolysis in cancer cells, suggesting a route toward a monitoring strategy. In this study, we took a multimodality approach to evaluate the phenotypic effects and metabolic changes that occur with inhibition of the PI3K/mTOR pathway. Its central role in regulating glycolysis in human sarcomas was evaluated by short- and long-term rapamycin treatment in sarcoma cell lines. We observed an overall decrease in lactate production in vitro, followed by cell growth inhibition. In vivo, we observed a similar quantitative reduction in lactate production as monitored by hyperpolarized MRI, also followed by tumor size changes. This noninvasive imaging method could distinguish reduced cell proliferation from induction of cell death. Our results illustrate the use of hyperpolarized MRI as a sensitive technique to monitor drug-induced perturbation of the PI3K/mTOR pathway in sarcomas. Cancer Res; 77(11); 3113-20. ©2017 AACR.

ATAD3 gene cluster deletions cause cerebellar dysfunction associated with altered mitochondrial DNA and cholesterol metabolism.

Although mitochondrial disorders are clinically heterogeneous, they frequently involve the central nervous system and are among the most common neurogenetic disorders. Identifying the causal genes has benefited enormously from advances in high-throughput sequencing technologies; however, once the defect is known, researchers face the challenge of deciphering the underlying disease mechanism. Here we characterize large biallelic deletions in the region encoding the ATAD3C, ATAD3B and ATAD3A genes. Although high homology complicates genomic analysis of the ATAD3 defects, they can be identified by targeted analysis of standard single nucleotide polymorphism array and whole exome sequencing data. We report deletions that generate chimeric ATAD3B/ATAD3A fusion genes in individuals from four unrelated families with fatal congenital pontocerebellar hypoplasia, whereas a case with genomic rearrangements affecting the ATAD3C/ATAD3B genes on one allele and ATAD3B/ATAD3A genes on the other displays later-onset encephalopathy with cerebellar atrophy, ataxia and dystonia. Fibroblasts from affected individuals display mitochondrial DNA abnormalities, associated with multiple indicators of altered cholesterol metabolism. Moreover, drug-induced perturbations of cholesterol homeostasis cause mitochondrial DNA disorganization in control cells, while mitochondrial DNA aggregation in the genetic cholesterol trafficking disorder Niemann-Pick type C disease further corroborates the interdependence of mitochondrial DNA organization and cholesterol. These data demonstrate the integration of mitochondria in cellular cholesterol homeostasis, in which ATAD3 plays a critical role. The dual problem of perturbed cholesterol metabolism and mitochondrial dysfunction could be widespread in neurological and neurodegenerative diseases.

Harnessing Preclinical Molecular Imaging to Inform Advances in Personalized Cancer Medicine.

Comprehensive molecular analysis of individual tumors provides great potential for personalized cancer therapy. However, the presence of a particular genetic alteration is often insufficient to predict therapeutic efficacy. Drugs with distinct mechanisms of action can affect the biology of tumors in specific and unique ways. Therefore, assays that can measure drug-induced perturbations of defined functional tumor properties can be highly complementary to genomic analysis. PET provides the capacity to noninvasively measure the dynamics of various tumor biologic processes in vivo. Here, we review the underlying biochemical and biologic basis for a variety of PET tracers and how they may be used to better optimize cancer therapy.

Global Analysis of Protein Expression and Phosphorylation Levels in Nicotine-Treated Pancreatic Stellate Cells.

Smoking is a risk factor in pancreatic disease; however, the biochemical mechanisms correlating smoking with pancreatic dysfunction remain poorly understood. Strategies using multiplexed isobaric tag-based mass spectrometry facilitate the study of drug-induced perturbations on biological systems. Here, we present the first large-scale analysis of the proteomic and phosphoproteomic alterations in pancreatic stellate cells following treatment with two nicotinic acetylcholine receptor (nAChR) ligands: nicotine and α-bungarotoxin. We treated cells with nicotine or α-bungarotoxin for 12 h in triplicate and compared alterations in protein expression and phosphorylation levels to mock-treated cells using a tandem mass tag (TMT9plex)-based approach. Over 8100 proteins were quantified across all nine samples, of which 46 were altered in abundance upon treatment with nicotine. Proteins with increased abundance included those associated with neurons, defense mechanisms, indicators of pancreatic disease, and lysosomal proteins. In addition, we measured differences for ∼16 000 phosphorylation sites across all nine samples using a titanium dioxide-based strategy, of which 132 sites were altered with nicotine and 451 with α-bungarotoxin treatment. Many altered phosphorylation sites were involved in nuclear function and transcriptional events. This study supports the development of future targeted investigations to establish a better understanding for the role of nicotine and associated receptors in pancreatic disease.

MEK Inhibition Overcomes Cisplatin Resistance Conferred by SOS/MAPK Pathway Activation in Squamous Cell Carcinoma.

Genomic analyses of squamous cell carcinoma (SCC) have yet to yield significant strategies against pathway activation to improve treatment. Platinum-based chemotherapy remains the mainstay of treatment for SCC of different histotypes either as a single-agent or alongside other chemotherapeutic drugs or radiotherapy; however, resistance inevitably emerges, which limits the duration of treatment response. To elucidate mechanisms that mediate resistance to cisplatin, we compared drug-induced perturbations to gene and protein expression between cisplatin-sensitive and -resistant SCC cells, and identified MAPK-ERK pathway upregulation and activation in drug-resistant cells. ERK-induced resistance appeared to be activated by Son of Sevenless (SOS) upstream, and mediated through Bim degradation downstream. Clinically, elevated p-ERK expression was associated with shorter disease-free survival in patients with locally advanced head and neck SCC treated with concurrent chemoradiation. Inhibition of MEK/ERK, but not that of EGFR or RAF, augmented cisplatin sensitivity in vitro and demonstrated efficacy and tolerability in vivo. Collectively, these findings suggest that inhibition of the activated SOS-MAPK-ERK pathway may augment patient responses to cisplatin treatment.

A New System for Profiling Drug-Induced Calcium Signal Perturbation in Human Embryonic Stem Cell–Derived Cardiomyocytes

The emergence of human stem cell–derived cardiomyocyte (hSCCM)–based assays in the cardiovascular (CV) drug discovery sphere requires the development of improved systems for interrogating the rich information that these cell models have the potential to yield. We developed a new analytical framework termed SALVO (synchronization, amplitude, length, and variability of oscillation) to profile the amplitude and temporal patterning of intra- and intercellular calcium signals in hSCCM. SALVO quantified drug-induced perturbations in the calcium signaling “fingerprint” in spontaneously contractile hSCCM. Multiparametric SALVO outputs were integrated into a single index of in vitro cytotoxicity that confirmed the rank order of perturbation as astemizole > thioridazine > cisapride > flecainide > valdecoxib > sotalol > nadolol ≈ control. This rank order of drug-induced Ca2+ signal disruption is in close agreement with the known arrhythmogenic liabilities of these compounds in humans. Validation of the system using a second set of compounds and hierarchical cluster analysis demonstrated the utility of SALVO to discriminate drugs based on their mechanisms of action. We discuss the utility of this new mechanistically agnostic system for the evaluation of in vitro drug cytotoxicity in hSCCM syncytia and the potential placement of SALVO in the early stage drug screening framework.

A Virtual B Cell Lymphoma Model to Predict Effective Combination Therapy

Aberrant activation of the B cell receptor (BCR) signaling network drives survival and proliferation of many B cell malignancies, such as activated B cell like diffuse large B cell lymphoma (ABC-DLBCL). A number of small molecule inhibitors targeting various kinases in the BCR signaling network have been developed. However, clinical application of these targeted agents is facing several challenges such as low response rate and acquired drug resistance. The limited efficacy of single agent targeted therapy is at least partially due to pathway reactivation through crosstalks and compensatory circuits. By simultaneously repressing multiple nodes in the signaling network, combination therapy has the potential to completely extinguish signaling and induce more potent and durable response. The complexity of BCR signaling network makes it difficult to infer which combinations will be effective and synergistic. Given the large number of possible drug combinations, comprehensive experimental screening – including exploration of multiple dosages – is not practically feasible.Computational models of signaling networks that can accurately reconstruct signaling dynamics in silico may represent a useful alternative to experimental screening and trial-and-error experimental investigation. Here, we developed a computational model that integrates signal transduction, tumor growth, and drug kinetics to accurately simulate BCR signaling dynamics and the effect of drug-induced perturbation on signaling output and cell viability. We used this model to predict effective combinatorial therapy in silicoand validated some of these predictions using in vitro experiments.Based on experimentally verified protein-protein interactions, we constructed the first detailed kinetic model of the BCR signaling network covering three major signaling pathways downstream of BCR, namely NFkB, PI3K/AKT and MAPK. The model captured complex crosstalk between these three pathways and multiple feedback loops. Simulated kinase activation time courses under temporal antigen stimulus successfully recapitulated normal BCR signaling dynamics as reported in literature.Using published drug response data in the BCR signaling-dependent ABC-DLBCL cell line TMD8, we trained a tumor growth model which in combination with the kinetic model enabled reliable prediction of viability response of many drug combinations at various dosages. For example, predicted viability response of BTK inhibitor ibrutinib in combination with inhibitors targeting other kinases in the network, e.g. BKM-120 against PI3K, sotrastaurin against PKC-beta closely matched previously published experimental data in TMD8 (r>0.86,p<1e-11).We then sought to identify synergistic drug combinations by simulating viability response at 10x10 virtual dosages for each drug combination and estimating synergism using Bliss independence model. Computational screening predicted dual blockage of LYN and SYK as the most synergistic combination, which we confirmed experimentally by treating TMD8 cells with LYN inhibitor Dasatinib and SYK inhibitor R406 at multiple doses.Finally we sought to use our model to predict biomarkers of sensitivity and resistance to specific treatment strategies. By integrating expression levels of BCR signaling network components assessed by published primary DLBCL RNA-seq data, we simulated patient-specific drug responses and computed the correlation between expression level of specific components with viability response under specific treatments. We found that overexpression of PTP1B, which dephosphorylates BTK substrate PLCg2, predicts relative sensitivity to BTK inhibition. Supporting this prediction, we observed increased PTP1B expression in DLBCL cell lines sensitive to ibrutinib treatment, suggesting PTP1B as potential biomarker for ibrutinib sensitivity.In summary, this study provides a novel approach to computationally optimize combinatorial targeted therapy against aberrant BCR signaling and paves the way for the discovery of effective patient-specific drug combinations. DisclosuresMelnick:Bioreference: Scientific Advisory Board, Scientific Advisory Board Other; Calgene: Consultancy; Janssen: Research Funding; Genentech: Speakers Bureau.

Elucidating the genotype-phenotype relationships and network perturbations of human shared and specific disease genes from an evolutionary perspective.

To date, numerous studies have been attempted to determine the extent of variation in evolutionary rates between human disease and nondisease (ND) genes. In our present study, we have considered human autosomal monogenic (Mendelian) disease genes, which were classified into two groups according to the number of phenotypic defects, that is, specific disease (SPD) gene (one gene: one defect) and shared disease (SHD) gene (one gene: multiple defects). Here, we have compared the evolutionary rates of these two groups of genes, that is, SPD genes and SHD genes with respect to ND genes. We observed that the average evolutionary rates are slow in SHD group, intermediate in SPD group, and fast in ND group. Group-to-group evolutionary rate differences remain statistically significant regardless of their gene expression levels and number of defects. We demonstrated that disease genes are under strong selective constraint if they emerge through edgetic perturbation or drug-induced perturbation of the interactome network, show tissue-restricted expression, and are involved in transmembrane transport. Among all the factors, our regression analyses interestingly suggest the independent effects of 1) drug-induced perturbation and 2) the interaction term of expression breadth and transmembrane transport on protein evolutionary rates. We reasoned that the drug-induced network disruption is a combination of several edgetic perturbations and, thus, has more severe effect on gene phenotypes.

Drug induced changes in the mouse liver epigenome

Epigenetic reprogramming involves the erasure and re-establishment of DNA and chromatin modifications during mammalian development. Once re-established the epigenome contributes to the maintenance of tissue identity. Perturbations in the epigenome are associated with exposure to a range of drugs and toxicants, including non-genotoxic carcinogens. Application of genome-wide integrated epigenomic and transcriptomic studies reveals the extent and dynamic nature of epigenetic perturbations resulting from xenobiotic exposure. We have demonstrated that exposure to the drug phenobarbital (PB) perturbs normal 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) profiles in a dose dependent manner. The PB response gene Cyp2b10 undergoes a rapid transcriptional activation alongside a parallel active demethylation (5mC > 5hmC > C) event at its promoter region. We observe numerous changes in the two DNA modifications, which are dynamic and reciprocal in nature, with frequent losses in 5hmC observed at and around the transcriptional start sites of many genes. Dynamic changes in promoter 5hmC and 5mC can be related to a global response to PB exposure that is independent of transcriptional changes and links with cancer associated DNA modification reprogramming profiles. Our study illustrates the potential for 5mC/5hmC profiling at promoter regions to identify potential drivers of non-genotoxic carcinogenesis in the mouse liver. Drug-induced perturbations of the epigenome generate unique epigenetic signatures that can enhance our mechanistic understanding of xenobiotic exposure, and potentially lead to the identification of novel safety biomarkers.